Head Computed Tomography Imaging Research Articles

Characterization of Visual Pathway Abnormalities in Infants With Congenital Zika Syndrome Using Computed Tomography and Magnetic Resonance Imaging.

Severe visual impairment is present in nearly all infants with congenital Zika syndrome (CZS); however, ocular abnormalities are present only in a subset of these infants. The purpose of this study was to characterize the visual pathway abnormalities seen on computed tomography (CT) and MRI scans in infants with CZS. Preliminary neuroimaging information was obtained from a referred sample of 105 infants with clinical and epidemiologic data consistent with CZS in the Pernambuco state of Brazil. Subjects were excluded if Zika virus infection was not confirmed by serologic or cerebrospinal fluid studies or if images were nondiagnostic. Of the 105 subjects initially screened, head CT images adequate for interpretation were available for 54, and brain MRI images adequate for interpretation were available for 20. Four patients had both CT and MRI images. Magnetic resonance imaging and CT scans from infants with CZS were systematically reviewed for globe malformations, optic nerve and chiasmal atrophy, occipital cortical volume loss, white matter abnormalities, ventriculomegaly, and calcifications. Neuroimaging findings were correlated with measures of visual function and with ocular examinations in these infants. Thirty-three males and 37 females were included in the analysis. The mean age of the infants at the time of neuroimaging was 16.0 weeks (range 0 days-15.5 months), and the mean gestational age at the time of birth was 38 weeks. All patients were from the Pernambuco state of Brazil. Overall, 70 of 74 (95%) scans showed occipital volume loss, whereas 9 (12%) showed optic nerve atrophy, 3 (4%) showed chiasmal atrophy, and 1 (1%) showed an ocular calcification. Sixty-two of the infants underwent ophthalmologic examinations. A total of 34 (55%) infants had at least one documented structural ocular abnormality, and 26 (42%) had at least one structural ocular abnormality documented in both eyes. Of those with available visual acuity data, all had visual impairment. Among those with visual impairment and normal eye examinations, 100% had visual pathway abnormalities on neuroimaging, including 100% with occipital cortical volume loss, 8% with optic nerve atrophy, and 8% with chiasmal atrophy. Our results suggest that cortical visual impairment related to structural abnormalities of the occipital cortex is likely an important cause of visual impairment in children with CZS with normal eye examinations.

Indications for Computed Tomography in Older Adult Patients With Minor Head Injury in the Emergency Department.

Older age is a risk factor for intracranial injury after head trauma, and computed tomography (CT) is generally recommended. We aimed to develop a clinical prediction rule for risk stratification to avoid CT head imaging in older adult patients with minor head injury, named Computed Tomography of the Head for the patients at Advanced age (CTHEAD). This was a single-center observational study in Japan that used retrospective chart review data to service a prediction rule that was prospectively validated. Patients aged≥65years who presented to our emergency department with a chief complaint of head trauma and a Glasgow Coma Scale (GCS) of≥13 were eligible. Patients were excluded if they had GCS score<13, anticoagulant therapy, focal neurologic symptoms, posttraumatic seizures, penetrating injury, evident depressed fracture, unknown mechanism, or CT not undertaken. The primary outcome was acute traumatic lesion on head CT. We screened 1,494 patients; 538 were included in the derivation cohort, and 580, in the validation cohort. Multivariable analysis of the derivation group found that high-risk mechanisms of injury, vomiting, witnessed loss of consciousness, and anterograde amnesia were significantly associated with traumatic head findings on CT. A clinical prediction rule was developed from these four risk factors. The negative predictive value (NPV) of the absence of the four components was 95.1% (95% confidence interval [CI]= 92.9% to 96.7%), and the positive predictive value (PPV) of one or more risk factors was 20.6% (95% CI= 17.3% to 24.4%). The rule was validated prospectively with an NPV of zero risk factors of 94.5% (95% CI= 92.4% to 96.1%) and a PPV of one or more risk factors of 15.9% (95% CI= 13.0% to 19.3%). Fifty-three (9.1%) patients in the validation cohort experienced the primary outcome. The results suggest that older adult patients with minor head injury may forgo head CT if they do not have high-risk mechanisms of injury, vomiting, witnessed loss of consciousness, or anterograde amnesia. External validation of this rule is needed.

Approach to Brain Magnetic Resonance Imaging for Non-Radiologists

The goal of this review is to provide a guide to magnetic resonance imaging (MRI) reading for non-radiologists. A thorough literature search was conducted using the keywords “MRI”, “CT”, “Non-radiologist” and “MRI interpretation” to develop an approach to MRI reading for non-radiologists. Common indications for a brain MRI include workup of an intracranial tumor, chronic headache, seizure disorder, and confirmation of a stroke. When assessing for an intracranial tumor, MRI is the preferred diagnostic modality. Computed tomography (CT) has much lower resolution and is typically reserved for the emergency setting. T1 weighted images provide anatomically relevant images of the brain parenchyma that will be familiar to non-radiologists. In contrast to T1 weighted images, fluid is bright in T2 and white matter will appear darker than gray matter. Fluid attenuation inversion recovery (FLAIR) is most sensitive for edema and parenchymal abnormalities like a low-grade glioma. The main purpose of diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) sequences are to visualize acute ischemic stroke. Although non-radiologists generally have a greater exposure to head CT images, the same foundational principles of CT head interpretation can apply to brain MRI reading. Benefits of brain imaging by MRI includes obtaining a multi-planar assessment of the brain, highly detailed images of the brain, and using different MRI sequences to assess for different pathology. J Neurol Res. 2020;10(5):173-176 doi: https://doi.org/10.14740/jnr628

Automatic detection of acute ischemic stroke using non-contrast computed tomography and two-stage deep learning model

Background and objectiveCurrently, it is challenging to detect acute ischemic stroke (AIS)-related changes on computed tomography (CT) images. Therefore, we aimed to develop and evaluate an automatic AIS detection system involving a two-stage deep learning model. MethodsWe included 238 cases from two different institutions. AIS-related findings were annotated on each of the 238 sets of head CT images by referring to head magnetic resonance imaging (MRI) images in which an MRI examination was performed within 24 h following the CT scan. These 238 annotated cases were divided into a training set including 189 cases and test set including 49 cases. Subsequently, a two-stage deep learning detection model was constructed from the training set using the You Only Look Once v3 model and Visual Geometry Group 16 classification model. Then, the two-stage model performed the AIS detection process in the test set. To assess the detection model's results, a board-certified radiologist also evaluated the test set head CT images with and without the aid of the detection model. The sensitivity of AIS detection and number of false positives were calculated for the evaluation of the test set detection results. The sensitivity of the radiologist with and without the software detection results was compared using the McNemar test. A p-value of less than 0.05 was considered statistically significant. ResultsFor the two-stage model and radiologist without and with the use of the software results, the sensitivity was 37.3%, 33.3%, and 41.3%, respectively, and the number of false positives per one case was 1.265, 0.327, and 0.388, respectively. On using the two-stage detection model's results, the board-certified radiologist's detection sensitivity significantly improved (p-value = 0.0313). ConclusionsOur detection system involving the two-stage deep learning model significantly improved the radiologist's sensitivity in AIS detection.

Deep learning vs. atlas-based models for fast auto-segmentation of the masticatory muscles on head and neck CT images

BackgroundImpaired function of masticatory muscles will lead to trismus. Routine delineation of these muscles during planning may improve dose tracking and facilitate dose reduction resulting in decreased radiation-related trismus. This study aimed to compare a deep learning model with a commercial atlas-based model for fast auto-segmentation of the masticatory muscles on head and neck computed tomography (CT) images.Material and methodsPaired masseter (M), temporalis (T), medial and lateral pterygoid (MP, LP) muscles were manually segmented on 56 CT images. CT images were randomly divided into training (n = 27) and validation (n = 29) cohorts. Two methods were used for automatic delineation of masticatory muscles (MMs): Deep learning auto-segmentation (DLAS) and atlas-based auto-segmentation (ABAS). The automatic algorithms were evaluated using Dice similarity coefficient (DSC), recall, precision, Hausdorff distance (HD), HD95, and mean surface distance (MSD). A consolidated score was calculated by normalizing the metrics against interobserver variability and averaging over all patients. Differences in dose (∆Dose) to MMs for DLAS and ABAS segmentations were assessed. A paired t-test was used to compare the geometric and dosimetric difference between DLAS and ABAS methods.ResultsDLAS outperformed ABAS in delineating all MMs (p < 0.05). The DLAS mean DSC for M, T, MP, and LP ranged from 0.83 ± 0.03 to 0.89 ± 0.02, the ABAS mean DSC ranged from 0.79 ± 0.05 to 0.85 ± 0.04. The mean value for recall, HD, HD95, MSD also improved with DLAS for auto-segmentation. Interobserver variation revealed the highest variability in DSC and MSD for both T and MP, and the highest scores were achieved for T by both automatic algorithms. With few exceptions, the mean ∆D98%, ∆D95%, ∆D50%, and ∆D2% for all structures were below 10% for DLAS and ABAS and had no detectable statistical difference (P > 0.05). DLAS based contours had dose endpoints more closely matched with that of the manually segmented when compared with ABAS.ConclusionsDLAS auto-segmentation of masticatory muscles for the head and neck radiotherapy had improved segmentation accuracy compared with ABAS with no qualitative difference in dosimetric endpoints compared to manually segmented contours.

Machine Learning for Identification of Craniomaxillofacial Radiographic Lesions

Machine Learning for Identification of Craniomaxillofacial Radiographic Lesions

Postcardiac arrest neurological prognostication with quantitative regional cerebral densitometry

PurposeTo quantitatively assess the severity of anoxic-ischemic brain injury early after cardiac arrest (CA) using a novel automated method applied to head computed tomography (HCT). MethodsAdult patients who were comatose and underwent HCT < 24 h after arrest were included in a retrospective analysis. Principal endpoint was unfavorable outcome (UO) defined as Cerebral Performance Category (CPC) of 3–5 at hospital discharge. We developed an automated processing algorithm for HCT images to be registered, atlas-segmented in 181 regions, and region-specific radiologic densities determined in Hounsfield Units. This approach was compared with an established manual method evaluating grey-white matter ratios (GWR). We tested univariable and multivariable prognostic models which integrated clinical and HCT features including densities in lobes and in nodes of cerebral networks linked to CA recovery. ResultsNinety-one patients were enrolled among whom 66 (73%) had an UO. HCTs were interpreted as normal or without acute abnormality by a neuroradiologist in 77 cases (85%). Compared to the favorable outcome group, UO patients had significantly lower densities in all lobes and in nodes of cerebral networks. A model combining clinical variables with the automated method applied to cerebral network nodes had the highest prognostic performance although not significantly different than the combined clinical-GWR method (AUC [95% CI] 0.94 [0.86−1.00] and 0.92 [0.83−1.00] respectively). ConclusionIn comatose survivors of CA, automated quantitative analysis of HCT revealed very early multifocal changes in brain tissue density which are mostly overlooked on conventional neuroradiologic interpretation and are associated with neurological outcome.

Optimize non-contrast head CT imaging tasks using multiple virtual monochromatic image sets in dual-energy spectral CT.

To demonstrate the value of optimizing non-contrast head imaging tasks by using the multiple virtual monochromatic spectral imaging (VMSI) sets in dual-energy spectral computed tomography (CT) and determine the optimal energy levels for various tasks in imaging head. This retrospective study includes 55 patients (29 women and 26 men, average age of 54.5±12.3years) who underwent non-contrast head CT in spectral imaging mode. For each patient, 21 VMSI sets from 40 to 140 keV at 5 keV intervals and 120 kVp-like image were reconstructed. Signal-to-noise ratio (SNR) for gray matter (GM) and white matter (WM), contrast-to-noise ratio (CNR) between GM and WM, subcalvarial artifact index (SAI) and posterior fossa artifact index (PFAI) were measured by placing regions-of-interest (ROIs) in nine areas of the brain. Two radiologists subjectively rated the image quality in terms of GM-WM differentiation, beam hardening artifacts in the subcalvarial space, and posterior fossa. The overall image noise was evaluated using a 5-point Likert scale. Measurement data were then statistically analyzed. The optimal energy level for viewing the supratentorial brain and reducing beam hardening artifacts in the subcalvarial space and posterior fossa was 65 keV and 75 keV, respectively. All corresponding measurements at these energy levels were significantly better than those measured at other energy levels (all P < 0.05). Dual-energy spectral CT imaging provides a set of virtual monochromatic spectral imaging and can be selected on-demand to optimize the imaging tasks in non-contrast head CT. VMSI reconstruction of dual-energy unenhanced head CT scans at 65-75 keV enables to maximize image quality.

Radiologic analysis of the location, shape and size of the external aperture of the carotid canal in children

This retrospective computed tomography (CT) study was aimed to assess the growth dynamic of the external aperture of the carotid canal (EACC) in children aged between 1 and 20years. Two hundred patients (sex 100 females/100 males, average age 10.50 ± 5.77years) with good head CT image quality were included in this study. CT images of the patients were used to obtain data related to the location, shape and dimension of EACC. EACC shapes were identified as oval shaped, round shaped, and tear-drop shaped in 58.3% (233 sides), 24% (96 sides) and 17.8% (71 sides), respectively. EACC length, disEACC-MSP (distance between EACC and midsagittal plane), and EACC width did not change from the prepubescence period; while, the disEACC-SC (distance between EACC and supramastoid crest) seemed to reach adult size in the postpubescence period. Linear functions for EACC length and width were calculated as: y = 5.453 + 0.091 × years, and y = 5.398 + 0.059 × years, respectively. The regression equations of the measured parameters representing the growth dynamic of EACC in children can be helpful to estimate its size, location and angulation, which suggest that the dimension and distances to certain anatomical landmarks seemed to reach adult size in different developmental periods. In this context, the findings of this study may seem to emphasize the importance of preoperative radiological evaluation on skull base, related to EACC, for multidisciplinary surgeon teams during childhood surgeries in terms of patients' positioning, and the selection of appropriate surgical approach.

Validation of cone beam computed tomography-based classification method for individual assessment of mid-palatal suture with respect to clinical application among Indian children.

: The aim of this study is to validate the Fernanda Angelieri classification method for the individual assessment of mid-palatal suture among Indian children using multi-slice computed tomography (CT) with respect to clinical application in rapid maxillary expansion. Present study was conducted utilizing 760 existing head and neck CT image records. CT images were stratified into three categories based on growth spurts: Group I - 6-10 years (n = 210), Group II - 11-14 years (n = 270), and Group III - 15-18 years (n = 280). The CT images were analyzed for stages of mid-palatal suture maturation, according to age and sex. The recorded data were subjected for statistical analysis. Indian children, up to age of 10 years, were distributed in stage A and B. After 11 years, up to 14 years girls showed varied distribution and spread equally among stage B, C, and D. After 15 years, more number of girls were distributed in stage D and E, whereas boys remained distributed variedly in stage B, C, and D. Mid- palatal suture maturational stages correlated to chronological age among both the genders using Fisher exact test and expected contingency table showed statistically significant variation among both the gender independently and collectively (P < 0.001). Cone beam computed tomography (CBCT)-based prediction of stages of mid- palatal suture maturation by Fernanda Angelieri is valid among the Indian population. During treatment plan of maxillary expansion among children above 10 years, it is better to have a diagnostic CBCT image analysis of mid-palate suture for predicable prognosis.

Approach to Brain Magnetic Resonance Imaging for Non-Radiologists

The goal of this review is to provide a guide to magnetic resonance imaging (MRI) reading for non-radiologists. A thorough literature search was conducted using the keywords “MRI”, “CT”, “Non-radiologist” and “MRI interpretation” to develop an approach to MRI reading for non-radiologists. Common indications for a brain MRI include workup of an intracranial tumor, chronic headache, seizure disorder, and confirmation of a stroke. When assessing for an intracranial tumor, MRI is the preferred diagnostic modality. Computed tomography (CT) has much lower resolution and is typically reserved for the emergency setting. T1 weighted images provide anatomically relevant images of the brain parenchyma that will be familiar to non-radiologists. In contrast to T1 weighted images, fluid is bright in T2 and white matter will appear darker than gray matter. Fluid attenuation inversion recovery (FLAIR) is most sensitive for edema and parenchymal abnormalities like a low-grade glioma. The main purpose of diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) sequences are to visualize acute ischemic stroke. Although non-radiologists generally have a greater exposure to head CT images, the same foundational principles of CT head interpretation can apply to brain MRI reading. Benefits of brain imaging by MRI includes obtaining a multi-planar assessment of the brain, highly detailed images of the brain, and using different MRI sequences to assess for different pathology. J Neurol Res. 2020;10(5):173-176 doi: https://doi.org/10.14740/jnr628

Determination of the Radiation Dose Level in Different Slice Computerized Tomography

Today, many imaging tools have emerged in the medical field using ionizing radiation. Computed tomography (CT) is regarded the most creative and common modality among the other imaging devices. In CT imaging, radiation may scatter to the environment from the patient and the interaction medium. In this study, two CT scanners with different slice numbers were used to measure the scatter radiation dose to the environment. Alderson rando phantom was utilized in the acquired CT exams as human body simulation. Radiation dose levels at different distances were determined by placing thermoluminescence dosimeters in the surrounding environment (at different distances) during phantom’s head-neck and thorax CT imaging. As a result, it has been found that the radiation dose varies between 13.90±1.96 µSv and 96.79±11.12 µSv in head and neck CT imaging. While, in thorax CT scan, the radiation dose varies between 5.28±0.84 µSv and 20.63±1.76 µSv.

Regularized Three-Dimensional Generative Adversarial Nets for Unsupervised Metal Artifact Reduction in Head and Neck CT Images

The reduction of metal artifacts in computed tomography (CT) images, specifically for strong artifacts generated from multiple metal objects, is a challenging issue in medical imaging research. Although there have been some studies on supervised metal artifact reduction through the learning of synthesized artifacts, it is difficult for simulated artifacts to cover the complexity of the real physical phenomena that may be observed in X-ray propagation. In this paper, we introduce metal artifact reduction methods based on an unsupervised volume-to-volume translation learned from clinical CT images. We construct three-dimensional adversarial nets with a regularized loss function designed for metal artifacts from multiple dental fillings. The results of experiments using 915 CT volumes from real patients demonstrate that the proposed framework has an outstanding capacity to reduce strong artifacts and to recover underlying missing voxels, while preserving the anatomical features of soft tissues and tooth structures from the original images.

Three-dimensional distribution of cystic lesions in osteonecrosis of the femoral head

PurposeThe aim of this study was to investigate the location characteristics of cystic lesions in a three-dimensional context and discuss the mechanism of formation. MethodsA total of 155 femoral head computed tomography images from 94 patients diagnosed with stage II and III osteonecrosis of the femoral head were retrospectively reviewed. Three-dimensional structures of the femoral head including the cystic lesions and necrotic area were reconstructed. We divided each femoral head into eight regions to observe the positional relationship of the cystic lesions, normal areas, and necrotic areas. ResultsThe regional distribution revealed 14 (13%), 35 (32%), 9 (8%), 25 (23%), 6 (6%), 15 (14%), 4 (4%), and 0 (0%) cystic lesions in regions Ⅰ, Ⅱ, Ⅲ, Ⅳ, Ⅴ, Ⅵ, Ⅶ, and Ⅷ, respectively. The anteromedial zone, A (Ⅰ ​+ ​Ⅲ), contained 22% of the lesions, anterolateral zone, B (Ⅱ ​+ ​Ⅳ), contained 54%, posteromedial zone, C (Ⅴ +Ⅶ), contained 9% of the lesions, and posterolateral zone, D (Ⅵ ​+ ​Ⅷ), contained 15% of the lesions. Most of the cystic lesions (78%) were located between the normal and necrotic areas; 18% of cystic lesions were in the necrotic area ​and 4% were in the normal area. ConclusionsCystic lesions most often occur at the junction of the necrotic ​and normal areas and are most commonly located in the anterolateral femoral head, which is similar to the distribution of the stress concentration region. The translational potential of this articleThe study showed the location characteristics of cystic lesions in osteonecrosis of femoral head, which suggested that the formation of cystic lesions may be related to stress and could accelerate the collapse of femoral head. The results can support further research on cystic lesions and provide a reference for doctors' treatment strategies for patients with osteonecrosis of femoral head.

Optic Nerve Sheath Measurements by Computed Tomography to Predict Intracranial Pressure and Guide Surgery in Patients with Traumatic Brain Injury

Research has shown that the optic nerve sheath diameter (ONSD) is a good predictor of intracranial pressure (ICP) and may predict the need for surgery in patients with head injury. The objective was to test the value of ONSD in predicting the requirement for surgery in patients with traumatic brain injury (TBI). In this retrospective cohort study, we first verified the correlation between ICP and ONSD using data from 62 patients with TBI who had undergone ICP monitoring. Second, we analyzed head computed tomography images from patients with TBI who were admitted to the emergency department where patients had been divided into surgery or conservative treatment groups, dependent on the assessment of a neurosurgeon. The correlation between ICP and ONSD was measured using linear regression analysis. Biologistic and receiver operating characteristic curve analyses were used to test the diagnostic value of ONSD to predict surgery. ONSD was significantly correlated with ICP (r= 0.606; P < 0.01), and there was a significant linear regression equation (ŷ= 0.071× ICP+ 3.533; P < 0.01), with ONSD predicting the requirement for surgery in patients with TBI (area under the curve, 0.920; P < 0.01; 95% confidence interval, 0.877-0.962). ONSD measured via head computed tomography correlates with ICP and can predict the requirement for surgery in patients with TBI following admission to the emergency department.

Patient Safety Analysis in Radiation Burden of Head Computed Tomography Imaging in 1185 Neurosurgical Inpatients

We performed a retrospective analysis in a cohort of 1185 patients at our institution who were identified as undergoing ≥1 head computed tomography (CT) examinations during their inpatient stay on the neurosurgery service, to quantify the number, type, and associated radiation burden of head CT procedures performed by the neurosurgery service. CT procedure records and radiology reports were obtained via database search and directly validated against records retrieved from manual chart review. Next, dosimetry data from the head CT procedures were extracted via automated text mining of electronic radiology reports. Among 4510 identified adult head CT procedures, 88% were standard head CT examinations. A total of 3.65 ± 3.60 head CT scans were performed during an average adult admission. The most common primary diagnoses were neoplasms, trauma, and other hemorrhage. The median cumulative effective dose per admission was 5.66 mSv (range, 1.06-84.5 mSv; mean, 8.56 ± 8.95 mSv). The median cumulative effective dose per patient was 6.4 mSv (range, 1.1-127 mSv; mean, 9.26 ± 10.0 mSv). The median cumulative radiation burden from head CT imaging in our cohort equates approximately to a single chest CT scan, well within accepted limits for safe CT imaging in adults. Refined methods are needed to characterize the safety profile of the few pediatric patients identified in our study.

Targeted gold nanoparticles enable molecular CT imaging of head and neck cancer: An in vivo study.

The development of various cost-effective multifunctional contrast agent for specific targeting molecular imaging of tumors presents a great challenge. We report here the in vivo targeting imaging of folic acid (FA) gold nanoparticles (AuNPs) through cysteamine (Cys) linking for targeted of human nasopharyngeal head and neck cancer by computed tomography (CT). The toxicity of nanoparticles in kidney, heart, spleen, brain and liver was evaluated by H&E (hematoxylin and eosin) assay. We showed that the formed FA-Cys-AuNPs with an Au core size of ˜13 nm are non-cytotoxic in the particle concentration of 3 × 103 μg/ ml. The nude mice were scanned using a 64-slice CT scan with parameters (80 kVp, slice thickness: 0.625 mm, mAs: 200, pitch: 1). CT scan was performed before and after (Three and six hours) I.V (Intra Venous) injection of AuNPs and FA-Cys-AuNPs. The distribution of nanoparticles in the nude mice was evaluated by imaging and coupled plasma optical emission spectrometry (ICP-OES) analysis. The findings clearly illustrated that a small tumor, which is undetectable via computed tomography, is enhanced by X-ray attenuation and becomes visible (4.30-times) by the molecularly targeted AuNPs. It was further demonstrated that active tumor cells targeting (FA-Cys-AuNPs) is more specific and efficient (2.03-times) than passive targeting AuNPs. According to the results, FA-Cys-AuNPs can be employed as a promising contrast agent in CT scan imaging and maybe in radiotherapy that require enhanced radiation dose.

Automatic Masseter Thickness Measurement and Ideal Point Localization for Botulinum Toxin Injection.

Botulinum toxin injection is a highly efficacious treatment for masseter muscle hypertrophy, while manual injection point localization based on experience can be non-quantitative, subjective and therefore suboptimal and even side-effect risky. To address this important while challenging task, in this paper we present a methodology of automatic ideal point localization for botulinum toxin injection based on automatic segmentation, measurement and quantitative analysis. Specifically, we first present a novel three-dimensional (3D) fully convolutional neural network for fully automatic mandible and masseter regions of interest (RoI) localization and segmentation from head computed tomography (CT) images. Given the segmentation results, the ideal injection points on the face are located using ray casting based automatic thickness measurement. We conducted experiments on an internal dataset consisting of head CT images acquired from 53 patients to evaluate the segmentation performance and localization reliability. The results demonstrate that the segmentation framework outperforms the state-of-the-art method by a significant margin, and the localization system provides intuitive, interactive user interface and reliable injection point decisions.

Head Computed Tomography Images of HIV/AIDS Patients with Suspected Cerebral Toxoplasmosis in Dr. Soetomo General Hospital Surabaya

Introduction: Toxoplasma gondii is an intracellular pathogenic parasite with the majority of co-infections occurring in HIV/AIDS patients. This study assesses the head computed tomography (CT) images of cerebral toxoplasmosis in patients with HIV/AIDS.Methods: This study was a cross-sectional design using head CT images of 35 HIV/AIDS patients with suspected cerebral toxoplasmosis. Variables include lesion type, location, size, CD4 count, and therapeutic result with anti-cerebral toxoplasmosis. All data analysed descriptively.Results: From total 110, 35 patients met the inclusion criteria. 24 patients (68.6%) were male and 11 (31.4%) female, average age, was 36.1. 8 patients (22.3%) had lesions in cortical, 31 patients (88.6%) had &lt; 1 cm lesion. Single lesions mainly calcified and found in the right centrum semiovale while multiple lesions were subcortical. A hypodense lesion with rim or nodular contrast enhancement is found in 75% of patients with CD4 &gt; 200 in contrast to slight rim contrast enhancement and perifocal edema in patients with CD4 &lt; 200. 20 patients (57.4%) had improved condition after anti-toxoplasmosis therapy.Conclusion: Cerebral toxoplasmosis lesions in HIV/AIDS patients have various types of imaging findings, mostly multiple, with most frequent location being cortical and diameter &lt; 1 cm. Total recovery is achieved in the majority of patients with therapy.

Efficacy of head and neck computed tomography for skeletal muscle mass estimation in patients with head and neck cancer

ObjectivesDiminished skeletal muscle mass (SMM) is a reliable marker of poor survival outcomes in patients with cancer. SMM or body composition is generally assessed at the third lumbar vertebra (L3) by abdominal computed tomography (CT) scans, not routinely evaluated in patients with head and neck squamous cell carcinoma (HNSCC). Therefore, we evaluated the effectiveness of head and neck CT images to assess SMM in patients with HNSCC for predicting their overall survival. Materials and MethodsSMM was assessed in 305 consecutive patients with stage III–IV HNSCC by measuring the cross-sectional area (CSA) at the third lumbar and cervical (C3) vertebrae levels. A formula for predicting the L3 SMM was established using linear regression analysis obtained from C3 CSA and other clinical factors. The actual SMM CSAs measured at L3 level and those obtained from the prediction model were compared using correlation analysis. The predictive power of our formula for estimating overall survival was compared using C-index. ResultsMedian SMM CSAs at the L3 and C3 levels were 174.5 cm2 and 56.3 cm2, respectively, and were not strongly correlated (adjusted R2 = 0.421). Prediction model 2 included the strongest predictive factors including sex, age, weight, and C3 SMM CSA, and significantly increased the L3 SMM correlation power (adjusted R2 = 0.721). The C-index of the prediction model was 0.713 (95% confidence interval 0.692–0.747). ConclusionsHead and neck CT imaging might be useful to estimate L3 SMM and predict overall survival in HNSCC patients.